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What We Do

Investing in financial instruments and trading those instruments for profit is a serious undertaking that requires intense focus and specialization. There are hundreds of different strategies being pursued by myriad managers, everything from hedge funds to high frequency trading, where  large amounts of capital are invested in gaining an ‘edge’.


It is our thesis that big data can be harnessed to create a measurable improvement in investment performance while controlling and limiting volatility. Essentially, with better information we believe Virtuoso can deliver an increase in alpha at the same time we decrease beta in our investment vehicle.


Clearly Virtuoso is not alone in this pursuit as evidenced by the exponential rise in algorithm trading, trend following trading systems and the recent rise of ‘robo advisors’ among the major brokerage houses. Our approach, however, is both more sophisticated and more advanced than virtually any other firm and has the potential to deliver enormous and sustained performance advantages to our clients.


Virtuoso will leverage the Intellectual Property developed by Stephen Shellman, PhD and his firm Strategic Analysis Enterprises, Inc. and adapt that technology specifically for financial instruments under an exclusive, perpetual license. Dr. Shellman and SAE have been working under contract to the intelligence community since 2001 in developing sophisticated, predictive threat assessment technologies that address political systems, violent conflict and terrorism. Dr. Shellman has achieved an unmatched record of accuracy and his technology is used at the highest level of the US Government including the Department of State, Department of Defense and the Central Intelligence Agency.


Since 2008, more than $10,000,000 has been invested in developing and perfecting the SAE four part text analysis suite (Praxis, Pathos, Logos, Taxis) along with quantitative modeling and forecasting tools to visualize outputs and drive rapid decision-making. These automated data extraction tools are able to process a large volume of data in near real-time mode. Thus Virtuoso is able to dramatically compress the decision time lag from weeks and months down to hours. It is critically important to note that nearly all of the information developed by the Virtuoso machines will be information that is not yet reflected in market prices of traded securities. The premise that underpins the SAE approach is readily applicable to trading financial instruments and has been demonstrated, in limited tests, to deliver above market returns when deployed. Virtuoso is engaged in refining the technology, sourcing a higher volume of higher quality data and building the trading systems and risk controls required to launch a large scale